Regressor Based Estimation of the Eye Pupil Center

  • Necmeddin Said Karakoc
  • Samil Karahan
  • Yusuf Sinan Akgul
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

The locations of the eye pupil centers are used in a wide range of computer vision applications. Although there are successful commercial eye gaze tracking systems, their practical employment is limited due to required specialized hardware and extra restrictions on the users. On the other hand, the precision and robustness of the off the shelf camera based systems are not at desirable levels. We propose a general purpose eye pupil center estimation method without any specialized hardware. The system trains a regressor using HoG features with the distance between the ground-truth pupil center and the center of the train patches. We found HoG features to be very useful to capture the unique gradient angle information around the eye pupils. The system uses a sliding window approach to produce a score image that contains the regressor estimated distances to the pupil center. The best center positions of two pupils among the candidate centers are selected from the produced score images. We evaluate our method on the challenging BioID and Columbia CAVE data sets. The results of the experiments are overall very promising and the system exceeds the precision of the similar state of the art methods. The performance of the proposed system is especially favorable on extreme eye gaze angles and head poses. The results of all test images are publicly available.

References

  1. 1.
    Duchowski, A.: Eye Tracking Methodology: Theory and Practice. Springer, London (2007)MATHGoogle Scholar
  2. 2.
    Poole, A., Linden, B.J.: Eye tracking in HCI and usability research. In: Encyclopedia of Human Computer Interaction, pp. 211–219. Idea Group, Pennsylvania (2006)Google Scholar
  3. 3.
    Rayner, K., Rotello, C.M., Stewart, A.J., Keir, J., Duffy, S.A.: Integrating text and pictorial information: eye movements when looking at print advertisements. J. Exp. Psychol. Appl. 7(3), 219–226 (2001)CrossRefGoogle Scholar
  4. 4.
    Levine, J.: An eye-controlled computer. IBM Thomas J. Watson Research Center, Yorktown Heights, N.Y. (1982)Google Scholar
  5. 5.
    Li, D., Babcock, J., Parkhurst, D.J.: openEyes: a low-cost head-mounted eye-tracking solution. In: Proceedings of the 2006 Symposium on Eye Tracking Research & Applications (2006)Google Scholar
  6. 6.
    Ohno, T., Mukawa, N.: A free-head, simple calibration, gaze tracking system that enables gaze-based interaction. In: Proceedings of the 2004 Symposium on Eye Tracking Research & Applications (2004)Google Scholar
  7. 7.
    Valenti, R., Gevers, T.: Accurate eye center location through invariant isocentric patterns. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1785–1798 (2012)CrossRefGoogle Scholar
  8. 8.
    Hamouz, M., Josef, K., Kamarainen, J.-K., Pekka, P., Heikki, K., Jiri, M.: Feature-based affine-invariant localization of faces. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1490–1495 (2005)CrossRefGoogle Scholar
  9. 9.
    Wang, P., Ji, Q.: Multi-view face and eye detection using discriminant features. Comput. Vis. Image Underst. 105(2), 99–111 (2007)CrossRefGoogle Scholar
  10. 10.
    Markus, N., Frljak, M., Pandzic, I.S., Ahlberg, J., Forchheimer, R.: Eye pupil localization with an ensemble of randomized trees. Pattern Recogn. 47(2), 578–587 (2014)CrossRefGoogle Scholar
  11. 11.
    Timm, F., Barth, E.: Accurate eye centre localisation by means of gradients. In: VISAPP (2011)Google Scholar
  12. 12.
    Campadelli, P., Lanzarotti, R., Lipori, G.: Precise eye localization through a general-to-specific model definition. In: BMVC (2006)Google Scholar
  13. 13.
    Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)CrossRefGoogle Scholar
  14. 14.
    Sironi, A., Lepetit, V., Fua, P.: Multiscale centerline detection by learning a scale-space distance transform. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2014)Google Scholar
  15. 15.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2005)Google Scholar
  16. 16.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  17. 17.
    Drucker, H., Burges, C.J., Kaufman, L., Smola, A., Vapnik, V.: Machines, support vector regression. In: Advances in Neural Information Processing Systems, pp. 155–161 (1997)Google Scholar
  18. 18.
    Karakoc, N.S., Karahan, S., Akgul, Y.S.: Iterative estimation of the eye pupil center. In: Signal Processing and Communications Applications Conference (SIU), Turkey, In Turkish (2015)Google Scholar
  19. 19.
    Chen, S., Liu, C.: Precise eye detection using discriminating HOG features. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011, Part I. LNCS, vol. 6854, pp. 443–450. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Monzo, D., Albiol, A., Sastre, J., Albiol, A.A.: Precise eye localization using HOG descriptors. Mach. Vis. Appl. 22(3), 471–480 (2011)Google Scholar
  21. 21.
    BioID Image Dataset. https://www.bioid.com/About/BioID-Face-Database. Accessed May 2015
  22. 22.
    Oliver, J., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. In: Audio-and video-Based Biometric Person Authentication, pp. 90–95 (2001)Google Scholar
  23. 23.
    Estimation of The Eye Gaze Direction. http://vision.gyte.edu.tr/projects.php?id=14. Accessed July 2015
  24. 24.
    Smith, B.A., Yin, Q., Feiner, S.K., Nayar, S.K.: Gaze locking: passive eye contact detection for human-object interaction. In: Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology (2013)Google Scholar
  25. 25.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Necmeddin Said Karakoc
    • 1
  • Samil Karahan
    • 1
  • Yusuf Sinan Akgul
    • 1
  1. 1.GTU Vision LabGebze Technical UniversityGebzeTurkey

Personalised recommendations